Most businesses are not short of data; it is just scattered. Sales live in the CRM, stock in the ERP, production on machine screens and the budget in a spreadsheet, and before every management meeting someone stitches the pieces together by hand. In Türkiye the gap is still wide: according to TurkStat's ICT Usage Survey in Enterprises 2025, only 6.5% of enterprises with 10 or more employees used business intelligence (BI) software. Quality matters as much as quantity. Writing in MIT Sloan Management Review, data quality expert Thomas C. Redman estimates the cost of bad data at 15% to 25% of revenue for most companies.
This hub collects our data and analytics articles into a reading path from first steps to decision. Start with moving from Excel reports to BI, which explains when spreadsheets stop coping and how to plan the switch. Then read how to set KPIs to decide what to measure, and our guide to building a management BI dashboard to bring them together on one screen. Then refine the indicator set for the team the dashboard serves: sales dashboard metrics for sales, cash flow reporting automation for finance, and how to measure NPS and customer churn analysis on the customer side. When it is time to pick software, how to choose a BI tool makes the comparison easier.
Going deeper, the infrastructure comes into focus. Our data warehouse and ETL guide explains how to bring data from several sources into one place, and data quality and duplicate records covers how to make reports trustworthy. For organisations working with spatial data, GIS for municipalities explains how to set up a city information system, while drone NDVI analysis shows how crop health maps are produced and read in agricultural and land projects.
Once the infrastructure choices are clear, move on to the detailed questions. ETL vs ELT differences covers where transformation should happen, data lake vs data warehouse where raw and unstructured data should live, and data warehouse cost factors what drives the budget. For plants collecting machine and sensor data, a time-series database is a separate storage decision, and root cause analysis with manufacturing data shows how to use that data to find why breakdowns and scrap increase.
To decide who owns each figure, how it is defined and who can access it, start with our data governance framework. Read master data management (MDM) to tie customer and product codes to one record across systems, data catalog and data dictionary to keep the business glossary and technical definitions where everyone can find them, and self-service analytics governance to let users build their own reports within rules; on the marketing side, a customer data platform (CDP) merges customer records into one profile. Real-time reporting explains which metrics need to be live and which are better reported periodically. For teams working with field and land data, route optimisation covers planning for delivery and service crews, drone crop yield estimation covers forecasting the harvest before it happens, and drone stockpile volume measurement covers measuring stockpiles on construction sites and quarries. For water utilities, the water network GIS inventory guide explains how to build the pipe, valve and service connection layer, and for large farmland satellite imagery for agriculture shows how season-long monitoring complements drone flights.
For delivery, see our data and analytics services and our pages on BI dashboards, data warehousing and data governance and quality. Before you begin, write down the three figures that cause the most debate in management meetings, and note which system produces each one, who prepares it and how often. That short list will largely decide where you should start reading and what the first dashboard needs to cover.
Other guides that put data to work
- Production and operations: how to calculate OEE, AI demand forecasting and anomaly detection.
- Sales and data sources: CRM and the sales pipeline, API integration and when to replace spreadsheets with software.
- Field data: soil moisture sensors for precision agriculture.